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Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition.

Dami Jeong1, Byung-Gyu Kim1, Suh-Yeon Dong1

  • 1Department of IT Engineering, Sookmyung Women's University, 100 Chungpa-ro 47 gil, Yongsna-gu, Seoul 04310, Korea.

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PubMed
Summary

This study introduces a novel deep learning approach for facial expression recognition, achieving high accuracy. The method effectively combines appearance and geometric features for improved emotion understanding in affective computing.

Keywords:
deep learningdeep spatiotemporal networkfacial expression recognition (FER)geometric featurejoint fusion classifierlocal binary pattern (LBP) feature

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Area of Science:

  • Affective Computing
  • Computer Vision
  • Machine Learning

Background:

  • Facial expressions are a primary channel for human emotional communication.
  • Accurate facial expression recognition is crucial for advancing affective computing.
  • Existing methods may not fully capture the complex spatiotemporal dynamics of expressions.

Purpose of the Study:

  • To develop an efficient deep learning model for facial expression recognition.
  • To integrate both visual appearance and geometric facial landmark information.
  • To enhance the accuracy and robustness of emotion detection systems.

Main Methods:

  • Proposed a deep joint spatiotemporal feature extraction using 3D convolutional neural networks.
  • Utilized 23 dominant facial landmarks to analyze muscle movement and energy distribution.
  • Implemented a joint fusion classifier to synergistically combine appearance and geometric features.

Main Results:

  • Achieved high recognition accuracies: 99.21% on CK+, 87.88% on MMI, and 91.83% on FERA datasets.
  • Demonstrated significant performance improvement over existing methods, with at least a 4% increase in accuracy.
  • The joint fusion approach effectively complements individual feature extractors.

Conclusions:

  • The proposed deep joint spatiotemporal feature method offers a highly accurate solution for facial expression recognition.
  • Integrating appearance and geometric features via a joint fusion classifier enhances emotion recognition capabilities.
  • This approach represents a significant advancement in the field of affective computing.